Origami-Enhanced Mechanical Properties for Worm-Like Robot.

In recent years, the exploration of worm-like robots has garnered much attention for their adaptability in confined environments. However, current designs face challenges in fully utilizing the mechanical properties of structures/materials to replicate the superior performance of real worms. In this article, we propose an approach to address this limitation based on the stacked Miura origami structure, achieving the seamless integration of structural design, mechanical properties, and robotic functionalities, that is, the mechanical properties originate from the geometric design of the origami structure and at the same time serve the locomotion capability of the robot. Three major advantages of our design are: the implementation of origami technology facilitates a more accessible and convenient fabrication process for segmented robotic skin with periodicity and flexibility, as well as robotic bristles with anchoring effect; the utilization of the Poisson's ratio effect for deformation amplification; and the incorporation of localized folding motion for continuous peristaltic locomotion. Utilizing the high geometric designability inherent in origami, our robot demonstrates customizable morphing and quantifiable mechanical properties. Based on the origami worm-like robot prototype, we experimentally verified the effectiveness of the proposed design in realizing the deformation amplification effect and localized folding motion. By comparing this to a conventional worm-like robot with discontinuous deformation, we highlight the merits of these mechanical properties in enhancing the robot's mobility. To sum up, this article showcases a bottom-up approach to robot development, including geometric design, mechanical characterization, and functionality realization, presenting a unique perspective for advancing the development of bioinspired soft robots.

FMEA-based risk management improves the ability of oral healthcare personnel to prevent needlestick injuries.

OBJECTIVE: To explore the application value of the Failure Mode and Effects Analysis (FMEA) method in the risk management of needlestick injuries among oral healthcare personnel. METHODS: A total of 37 healthcare workers from the dental department of Zhujiang Hospital, Southern Medical University, were selected as study subjects. Routine risk management procedures were followed from January 2021 to December 2021, serving as the control group, while FMEA-based risk management was implemented from January 2022 to December 2022, representing the research group. The Risk Priority Number (RPN) was calculated, and interventions were implemented for the top five identified failure modes. The RPN score, incidence of needlestick injuries, healthcare personnel's knowledge and awareness levels, prevention behavior, and rate of satisfaction with management were compared between the two groups. RESULTS: FMEA-based risk management identified weak knowledge of protection, disorganized placement of sharp instruments, failure to adhere to operational standards, improper operational procedures, and insufficient regulations for preventing needlestick injuries as the top five failure modes. The RPN scores for these modes were significantly lower in the research group (P<0.05). The research group also experienced a lower frequency and incidence of needlestick injury (P<0.05), along with higher levels of healthcare knowledge, awareness of prevention, and prevention behavior (P<0.05). Additionally, satisfaction with management was higher in the research group compared to the control group (P<0.05). CONCLUSION: FMEA-based risk management can improve the ability of oral healthcare personnel to prevent needlestick injury, reduce the occurrence of such incidents, and enhance satisfaction with management. This approach holds promise for wider adoption.

Cellular Automata Inspired Multistable Origami Metamaterials for Mechanical Learning.

Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher-level computation, existing computational frameworks require the integration of large-scale networked logic gates, which places demanding requirements on the fabrication of materials counterparts and the propagation of signals. Inspired by cellular automata, a novel computational framework based on multistable origami metamaterials by incorporating reservoir computing is proposed, which can accomplish high-level computation tasks without the need to construct a logic gate network. This approach thus eliminates the demanding requirements for the fabrication of materials and signal propagation when constructing large-scale networks for high-level computation in conventional mechanical logic. Using the multistable stacked Miura-origami metamaterial as a validation platform, digit recognition is experimentally implemented by a single actuator. Moreover, complex tasks, such as handwriting recognition and 5-bit memory tasks, are also shown to be feasible with the new computation framework. The research represents a significant advancement in developing a new generation of intelligent materials with advanced computational capabilities. With continued research and development, these materials can have a transformative impact on a wide range of fields, from computational science to material mechano-intelligence technology and beyond.

Research on the Predictive Analysis of Park Landscape Design and Cost Based on RNN Model.

As people's awareness of the environment gradually increases and their requirements for the comfort of living space become higher, landscape design has also ushered in a golden period of development. With the increasing investment in landscape construction in urban development, the area of park green space has been increasing. A park is a place that provides recreation and relaxation for the public. However, the mere pursuit of landscape quality and artistic effects without effective cost control will eventually lead to a rise in construction costs. Therefore, this study explores the main influencing factors that lead to high park landscape costs by analyzing the current development of park landscape design. Based on the comprehensive analysis, a park landscape cost prediction model based on recurrent neural networks is proposed in order to better control the construction costs of park landscapes. This study applies advanced deep learning technology to the project management of park landscapes, which effectively improves the accuracy of cost prediction. In addition, an artificial bee colony algorithm is introduced to update the weights of the recurrent neural network, resulting in a globally optimal ABC-RNN prediction model. The experimental results show that the proposed ABC-RNN prediction model has higher prediction accuracy and stability than the commonly used prediction models.

Identification of piecewise linear dynamical systems using physically-interpretable neural-fuzzy networks: Methods and applications to origami structures.

Self-locking origami structures are characterized by their piecewise linear constitutive relations between force and deformation, which, in practice, are always completely opaque and unmeasurable: the number of piecewise segments, the positions of non-smooth points, and the linear parameters of each segment are unknown a priori. However, acquiring this information is of fundamental importance for understanding the origami structure's dynamic folding process and predicting its dynamic behaviors. This, therefore, arouses our interest in adopting a dynamical identification process to determine the model and to estimate the parameters. In this research, based on the piecewise linear assumption, a physically-interpretable neural-fuzzy network is built to correlate the measured input and output data. Unlike the conventional approaches, the constructed neural network possesses specific physical meaning of its components: the number of neurons relates to the number of piecewise segments, the coefficients of the local linear models relate to the parameters of the constitutive relations, and the validity functions relate to the positions of non-smooth points. By addressing several examples with different backgrounds, the network's underlying data training methods are illustrated, including the local linear optimization for linear parameters, nested optimization for nonlinear partitions, and Local Linear Model Tree optimization for model selection. Noting that the tackled origami problem holds strong universality in terms of the unknown piecewise characteristics, the proposed approach would thus provide an effective, generic, and physically significant means for handling piecewise linear dynamical systems and meanwhile bring fresh vitality to the artificial neural network research.

Effect of freeze-thaw cycling on grain size of biochar.

Biochar may improve soil hydrology by altering soil porosity, density, hydraulic conductivity, and water-holding capacity. These properties are associated with the grain size distributions of both soil and biochar, and therefore may change as biochar weathers. Here we report how freeze-thaw (F-T) cycling impacts the grain size of pine, mesquite, miscanthus, and sewage waste biochars under two drainage conditions: undrained (all biochars) and a gravity-drained experiment (mesquite biochar only). In the undrained experiment plant biochars showed a decrease in median grain size and a change in grain-size distribution consistent with the flaking off of thin layers from the biochar surface. Biochar grain size distribution changed from unimodal to bimodal, with lower peaks and wider distributions. For plant biochars the median grain size decreased by up to 45.8% and the grain aspect ratio increased by up to 22.4% after 20 F-T cycles. F-T cycling did not change the grain size or aspect ratio of sewage waste biochar. We also observed changes in the skeletal density of biochars (maximum increase of 1.3%), envelope density (maximum decrease of 12.2%), and intraporosity (porosity inside particles, maximum increase of 3.2%). In the drained experiment, mesquite biochar exhibited a decrease of median grain size (up to 4.2%) and no change of aspect ratio after 10 F-T cycles. We also document a positive relationship between grain size decrease and initial water content, suggesting that, biochar properties that increase water content, like high intraporosity and pore connectivity large intrapores, and hydrophilicity, combined with undrained conditions and frequent F-T cycles may increase biochar breakdown. The observed changes in biochar particle size and shape can be expected to alter hydrologic properties, and thus may impact both plant growth and the hydrologic cycle.

Biochar particle size, shape, and porosity act together to influence soil water properties.

Many studies report that, under some circumstances, amending soil with biochar can improve field capacity and plant-available water. However, little is known about the mechanisms that control these improvements, making it challenging to predict when biochar will improve soil water properties. To develop a conceptual model explaining biochar's effects on soil hydrologic processes, we conducted a series of well constrained laboratory experiments using a sand matrix to test the effects of biochar particle size and porosity on soil water retention curves. We showed that biochar particle size affects soil water storage through changing pore space between particles (interpores) and by adding pores that are part of the biochar (intrapores). We used these experimental results to better understand how biochar intrapores and biochar particle shape control the observed changes in water retention when capillary pressure is the main component of soil water potential. We propose that biochar's intrapores increase water content of biochar-sand mixtures when soils are drier. When biochar-sand mixtures are wetter, biochar particles' elongated shape disrupts the packing of grains in the sandy matrix, increasing the volume between grains (interpores) available for water storage. These results imply that biochars with a high intraporosity and irregular shapes will most effectively increase water storage in coarse soils.

Biochar-induced changes in soil hydraulic conductivity and dissolved nutrient fluxes constrained by laboratory experiments.

The addition of charcoal (or biochar) to soil has significant carbon sequestration and agronomic potential, making it important to determine how this potentially large anthropogenic carbon influx will alter ecosystem functions. We used column experiments to quantify how hydrologic and nutrient-retention characteristics of three soil materials differed with biochar amendment. We compared three homogeneous soil materials (sand, organic-rich topsoil, and clay-rich Hapludert) to provide a basic understanding of biochar-soil-water interactions. On average, biochar amendment decreased saturated hydraulic conductivity (K) by 92% in sand and 67% in organic soil, but increased K by 328% in clay-rich soil. The change in K for sand was not predicted by the accompanying physical changes to the soil mixture; the sand-biochar mixture was less dense and more porous than sand without biochar. We propose two hydrologic pathways that are potential drivers for this behavior: one through the interstitial biochar-sand space and a second through pores within the biochar grains themselves. This second pathway adds to the porosity of the soil mixture; however, it likely does not add to the effective soil K due to its tortuosity and smaller pore size. Therefore, the addition of biochar can increase or decrease soil drainage, and suggests that any potential improvement of water delivery to plants is dependent on soil type, biochar amendment rate, and biochar properties. Changes in dissolved carbon (C) and nitrogen (N) fluxes also differed; with biochar increasing the C flux from organic-poor sand, decreasing it from organic-rich soils, and retaining small amounts of soil-derived N. The aromaticity of C lost from sand and clay increased, suggesting lost C was biochar-derived; though the loss accounts for only 0.05% of added biochar-C. Thus, the direction and magnitude of hydraulic, C, and N changes associated with biochar amendments are soil type (composition and particle size) dependent.

[Cloning and sequencing of junction fragment with exon 51 deletion of Dystrophin gene].

To study the mechanism of Dystrophin gene deletion, we obtained the deletion junction fragment of exon 51 by inverse PCR and analyzed the sequence characteristic of breakpoints and deletion junction fragment. After the full sequence of intron 51 was finished, the rough site of breakpoint in intron 51 of a DMD patient with exon 51 deletion was detected by PCR with 9 pairs of primers spaced every 3-5 kb in intron 51. Then the junction fragment was amplified by nested inverse PCR. After sequencing the junction fragment, the 3' breakpoint and partial sequences of intron 50 were determined by comparing with the normal sequences in intron 51. The primer was designated to sequence intron 50 according to the sequence of junction fragment, and then the 5' breakpoint was determined. A total of 1,614 bp in intron 50 was sequenced. The 5' and 3' breakpoints were located in the THE-1 internal sequence (Transposon-like Human Element, THE) and L2 sequence respectively. There are 3 bp junctional homology and no errors near the junction point. This is the second report that the deletion breakpoint located directly in THE-1 sequence studied at the DNA level. We here firstly reported that there is a THE-1 sequence in intron 50. THE-1 and non-homologue end joining repair mechanism may be associated with the Dystrophin gene deletion.

[The full sequence of intron 51 of dystrophin gene and its characteristic of sequence].

OBJECTIVE: To finish the work of sequencing the full sequence of intron 51 of dystrophin gene and understand its characteristic of sequence. METHODS: The whole intron 51 was sequenced by primer walking. The sequencing results were analyzed by repeat sequences, matrix attachment region (MAR) and topoisomerase II cleavage sites. The residue sequences, after removal of the repetitive sequences, were subjected to the analysis of CpG islands, promoter, open reading frame (ORF) and unidentified low copy repeat sequence. RESULTS: The acquired intron 51 sequence was composed of 38725 bp. Repetitive sequences constituted 37.53% of total intron sequence. The overall G+C content of intron 51 was 36.34%. There are four potential MARs in intron 51. Three of them are clustered in the 12 kb region near exon 51. Numerous ORFs were found on both strands, but no homologues proteins were found in Genbank CDS transcriptional peptide, PDB, SwissProt, PIR and PRF databases. CONCLUSION: The expansion of intron 7 over the last 120 million years was mainly the result of L1 insertion into intron 7, and not all of repetitive sequences are associated with chromosomal rearrangement. No sequence of functional significance was found in intron 51. The results suggest that the cluster of MARs may be associated with the instability of intron 51.